Today, web browsing is a major activity performed by a user to view information. Often, the user may want to retrieve additional information related to a particular text article on a web page that the user is browsing. In order to retrieve the additional information, contextual searches are performed by allowing the user to input a search query to retrieve the additional information. Similarly, searches are performed to display contextual advertisements to the user based on keywords in the search query. Typically, the contextual searches and contextual advertisements are directed towards specific text displayed on the web page. Further, the keywords in the search query need to be specific to retrieve search results. In addition, it is observed that contextual search query volume is less. Consequently, click through rate (CTR) of the contextual search and the contextual advertisements are low.
A similar approach of contextual searches is performed over images. However, the additional information of images displayed to the user is found only on an image caption. Traditional techniques involves in annotating images. The annotated images allow the additional information to be associated with a particular point in the images. Further, annotating images establishes image-tagging where relevant advertisements are displayed on highlighted portions of the images. However, in many cases, accurately identifying the content of the annotated images requires human intervention. Moreover, in the traditional techniques, insufficient information is provided to the user. The CTR is low in the above techniques and hence monetization is on a lower side.
In light of the foregoing discussion, there is a need for an efficient method and system for providing contextual search on digital images to the user.